On the convergence and worst-case complexity of trust-region and regularization methods for unconstrained optimization. - Geovani Nunes Grapiglia; Jin Yun Yuan; Ya-Xiang Yuan (Math. Program., 2015)

Nonlinear Stepsize Control Algorithms: Complexity Bounds for First- and Second-Order Optimality. - Geovani Nunes Grapiglia; Jin Yun Yuan; Ya-Xiang Yuan (J. Optim. Theory Appl., 2016)

On the worst-case complexity of nonlinear stepsize control algorithms for convex unconstrained optimization. - Geovani Nunes Grapiglia; Jin Yun Yuan; Ya-Xiang Yuan (Optim. Methods Softw., 2016)

On the worst-case evaluation complexity of non-monotone line search algorithms. - Geovani Nunes Grapiglia; Ekkehard W. Sachs (Comput. Optim. Appl., 2017)

Regularized Newton Methods for Minimizing Functions with Hölder Continuous Hessians. - Geovani Nunes Grapiglia; Yurii E. Nesterov (SIAM J. Optim., 2017)

Improved optimization methods for image registration problems. - Ke Chen 002; Geovani Nunes Grapiglia; Jinyun Yuan; Daoping Zhang (Numer. Algorithms, 2019)

Accelerated Regularized Newton Methods for Minimizing Composite Convex Functions. - Geovani Nunes Grapiglia; Yurii E. Nesterov (SIAM J. Optim., 2019)

A subspace version of the Wang-Yuan Augmented Lagrangian-Trust Region method for equality constrained optimization. - Carina Moreira Costa; Geovani Nunes Grapiglia (Appl. Math. Comput., 2020)

Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order Derivatives. - Geovani Nunes Grapiglia; Yurii E. Nesterov (SIAM J. Optim., 2020)

Worst-case evaluation complexity of derivative-free nonmonotone line search methods for solving nonlinear systems of equations. - Geovani Nunes Grapiglia; Flávia Chorobura (Comput. Appl. Math., 2021)

A generalized worst-case complexity analysis for non-monotone line searches. - Geovani Nunes Grapiglia; Ekkehard W. Sachs (Numer. Algorithms, 2021)

On inexact solution of auxiliary problems in tensor methods for convex optimization. - Geovani Nunes Grapiglia; Yurii E. Nesterov (Optim. Methods Softw., 2021)

An adaptive trust-region method without function evaluations. - Geovani Nunes Grapiglia; Gabriel F. D. Stella (Comput. Optim. Appl., 2022)

A cubic regularization of Newton's method with finite difference Hessian approximations. - Geovani Nunes Grapiglia; Max L. N. Gonçalves; G. N. Silva (Numer. Algorithms, 2022)

Tensor methods for finding approximate stationary points of convex functions. - Geovani Nunes Grapiglia; Yurii E. Nesterov (Optim. Methods Softw., 2022)

A subgradient method with non-monotone line search. - Orizon Pereira Ferreira; Geovani Nunes Grapiglia; Elianderson M. Santos; João Carlos de Oliveira Souza (Comput. Optim. Appl., 2023)

An Adaptive Riemannian Gradient Method Without Function Evaluations. - Geovani Nunes Grapiglia; Gabriel F. D. Stella (J. Optim. Theory Appl., 2023)

Quadratic regularization methods with finite-difference gradient approximations. - Geovani Nunes Grapiglia (Comput. Optim. Appl., 2023)

Worst-case evaluation complexity of a quadratic penalty method for nonconvex optimization. - Geovani Nunes Grapiglia (Optim. Methods Softw., 2023)

Adaptive Third-Order Methods for Composite Convex Optimization. - Geovani Nunes Grapiglia; Yurii E. Nesterov (SIAM J. Optim., 2023)

Worst-case evaluation complexity of a derivative-free quadratic regularization method. - Geovani Nunes Grapiglia (Optim. Lett., 2024)

TRFD: A Derivative-Free Trust-Region Method Based on Finite Differences for Composite Nonsmooth Optimization. - D. Davar; Geovani Nunes Grapiglia (SIAM J. Optim., 2025)

Universal nonmonotone line search method for nonconvex multiobjective optimization problems with convex constraints. - Maria Eduarda Pinheiro; Geovani Nunes Grapiglia (Comput. Appl. Math., 2025)

First and Zeroth-Order Implementations of the Regularized Newton Method with Lazy Approximated Hessians. - Nikita Doikov; Geovani Nunes Grapiglia (J. Sci. Comput., 2025)

Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients. - Geovani Nunes Grapiglia; Daniel McKenzie (J. Optim. Theory Appl., 2026)